Ensemble long short-term memory (EnLSTM) network

Long short-term memory (LSTM) The long short-term memory (LSTM) is a special kind of recurrent neural network (Gers et al., 1999; Hochreiter & Schmidhuber, 1997), and is capable of processing sequential data with correlations between points that are far apart. On the one hand, similar to the standard recurrent neural network, the LSTM has a self-looped structure that allows the result of the previous step to participate in the calculation of the subsequent step. On the other hand, the LSTM possesses four interaction layers in its neurons, which makes it able to forget useless information and learn correlations between data points that are far away from each other in sequence. The LSTM is the state-of-the-art model for well log generation in previous studies (Zhang et al., 2018). This agrees well with the perspective of geoscience, since the well logs reflect a formation condition, which possesses internal continuity (spatial dependency). The sequential information in reservoirs is critical for well logs generation. Therefore, the LSTM constitutes the ideal foundation for building a new model for this type of geoscience problem.